A Multi-Modal Analysis of the Freezing of Gait Phenomenon in Parkinson’s Disease
Background: Freezing of Gait (FOG) is one of the most disabling motor complications of Parkinson’s disease, and consists of an episodic inability to move forward, despite the intention to walk. FOG increases the risk of falls and reduces the quality of life of patients and their caregivers. The phen...
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| Format: | Article |
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MDPI AG
2022-03-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/22/7/2613 |
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| author | Luca Mesin Paola Porcu Debora Russu Gabriele Farina Luigi Borzì Wei Zhang Yuzhu Guo Gabriella Olmo |
| author_facet | Luca Mesin Paola Porcu Debora Russu Gabriele Farina Luigi Borzì Wei Zhang Yuzhu Guo Gabriella Olmo |
| author_sort | Luca Mesin |
| collection | DOAJ |
| description | Background: Freezing of Gait (FOG) is one of the most disabling motor complications of Parkinson’s disease, and consists of an episodic inability to move forward, despite the intention to walk. FOG increases the risk of falls and reduces the quality of life of patients and their caregivers. The phenomenon is difficult to appreciate during outpatients visits; hence, its automatic recognition is of great clinical importance. Many types of sensors and different locations on the body have been proposed. However, the advantages of a multi-sensor configuration with respect to a single-sensor one are not clear, whereas this latter would be advisable for use in a non-supervised environment. Methods: In this study, we used a multi-modal dataset and machine learning algorithms to perform different classifications between FOG and non-FOG periods. Moreover, we explored the relevance of features in the time and frequency domains extracted from inertial sensors, electroencephalogram and skin conductance. We developed both a subject-independent and a subject-dependent algorithm, considering different sensor subsets. Results: The subject-independent and subject-dependent algorithms yielded accuracies of 85% and 88% in the leave-one-subject-out and leave-one-task-out test, respectively. Results suggest that the inertial sensors positioned on the lower limb are generally the most significant in recognizing FOG. Moreover, the performance impairment experienced when using a single tibial accelerometer instead of the optimal multi-modal configuration is limited to 2–3%. Conclusions: The achieved results disclose the possibility of getting a good FOG recognition using a minimally invasive set-up made of a single inertial sensor. This is very significant in the perspective of implementing a long-term monitoring of patients in their homes, during activities of daily living. |
| first_indexed | 2024-03-09T11:26:57Z |
| format | Article |
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| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-03-09T11:26:57Z |
| publishDate | 2022-03-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-d35ba521e044496cb1505242eaaea50d2023-12-01T00:02:02ZengMDPI AGSensors1424-82202022-03-01227261310.3390/s22072613A Multi-Modal Analysis of the Freezing of Gait Phenomenon in Parkinson’s DiseaseLuca Mesin0Paola Porcu1Debora Russu2Gabriele Farina3Luigi Borzì4Wei Zhang5Yuzhu Guo6Gabriella Olmo7Department of Electronics and Telecommunications, Politecnico di Torino, Corso Duca degli Abruzzi 24, 10129 Torino, ItalyDepartment of Electronics and Telecommunications, Politecnico di Torino, Corso Duca degli Abruzzi 24, 10129 Torino, ItalyDepartment of Electronics and Telecommunications, Politecnico di Torino, Corso Duca degli Abruzzi 24, 10129 Torino, ItalyNeurology Unit, Azienda Ospedaliera Universitaria di Sassari, Viale San Pietro 10, 07100 Sassari, ItalyDepartment of Control and Computer Engineering, Politecnico di Torino, Corso Duca degli Abruzzi 24, 10129 Torino, ItalyDepartment of Neurology, Neurobiology and Geriatrics, Beijing Institute of Geriatrics, Xuanwu Hospital of Capital Medical University, Beijing 100053, ChinaSchool of Automation Science and Electrical Engineering, Beihang University, Beijing 100191, ChinaDepartment of Control and Computer Engineering, Politecnico di Torino, Corso Duca degli Abruzzi 24, 10129 Torino, ItalyBackground: Freezing of Gait (FOG) is one of the most disabling motor complications of Parkinson’s disease, and consists of an episodic inability to move forward, despite the intention to walk. FOG increases the risk of falls and reduces the quality of life of patients and their caregivers. The phenomenon is difficult to appreciate during outpatients visits; hence, its automatic recognition is of great clinical importance. Many types of sensors and different locations on the body have been proposed. However, the advantages of a multi-sensor configuration with respect to a single-sensor one are not clear, whereas this latter would be advisable for use in a non-supervised environment. Methods: In this study, we used a multi-modal dataset and machine learning algorithms to perform different classifications between FOG and non-FOG periods. Moreover, we explored the relevance of features in the time and frequency domains extracted from inertial sensors, electroencephalogram and skin conductance. We developed both a subject-independent and a subject-dependent algorithm, considering different sensor subsets. Results: The subject-independent and subject-dependent algorithms yielded accuracies of 85% and 88% in the leave-one-subject-out and leave-one-task-out test, respectively. Results suggest that the inertial sensors positioned on the lower limb are generally the most significant in recognizing FOG. Moreover, the performance impairment experienced when using a single tibial accelerometer instead of the optimal multi-modal configuration is limited to 2–3%. Conclusions: The achieved results disclose the possibility of getting a good FOG recognition using a minimally invasive set-up made of a single inertial sensor. This is very significant in the perspective of implementing a long-term monitoring of patients in their homes, during activities of daily living.https://www.mdpi.com/1424-8220/22/7/2613Parkinson’s diseaseFreezing of Gaitmulti-modal analysisinertial sensorselectroencephalogram (EEG)skin conductance (SC) |
| spellingShingle | Luca Mesin Paola Porcu Debora Russu Gabriele Farina Luigi Borzì Wei Zhang Yuzhu Guo Gabriella Olmo A Multi-Modal Analysis of the Freezing of Gait Phenomenon in Parkinson’s Disease Sensors Parkinson’s disease Freezing of Gait multi-modal analysis inertial sensors electroencephalogram (EEG) skin conductance (SC) |
| title | A Multi-Modal Analysis of the Freezing of Gait Phenomenon in Parkinson’s Disease |
| title_full | A Multi-Modal Analysis of the Freezing of Gait Phenomenon in Parkinson’s Disease |
| title_fullStr | A Multi-Modal Analysis of the Freezing of Gait Phenomenon in Parkinson’s Disease |
| title_full_unstemmed | A Multi-Modal Analysis of the Freezing of Gait Phenomenon in Parkinson’s Disease |
| title_short | A Multi-Modal Analysis of the Freezing of Gait Phenomenon in Parkinson’s Disease |
| title_sort | multi modal analysis of the freezing of gait phenomenon in parkinson s disease |
| topic | Parkinson’s disease Freezing of Gait multi-modal analysis inertial sensors electroencephalogram (EEG) skin conductance (SC) |
| url | https://www.mdpi.com/1424-8220/22/7/2613 |
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